High performance and grid computing with reliability quality of service control

ABSTRACT

High performance computing (HPC) and grid computing processing for seismic and reservoir simulation are performed without impacting or losing processing time in case of failures. A Data Distribution Service (DDS) standard is implemented in High Performance Computing (HPC) and grid computing platforms, to avoid the shortcomings of current Message Passing Interface (MPI) communication between computing modules, and provide quality of service (QoS) for such applications. QoS properties of the processing can be controlled.

CROSS REFERENCED TO RELATED APPLICATIONS

This application is a continuation of and claims priority to,commonly-owned U.S. patent application Ser. No. 13/649,286, filed Oct.11, 2012, titled, “High Performance and Grid Computing With Quality ofService Control” and issued as U.S. Pat. No. 8,874,804 which claimspriority to U.S. Provisional Patent Application No. 61/545,766 filedOct. 11, 2011.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to high performance and grid computing ofdata for exploration and production of hydrocarbons, such ascomputerized simulation of hydrocarbon reservoirs in the earth,geological modeling, and processing of seismic survey data, and inparticular to quality of service (QoS) control of such computing.

2. Description of the Related Art

In the oil and gas industries, massive amounts of data are required tobe processed for computerized simulation, modeling and analysis forexploration and production purposes. For example, the development ofunderground hydrocarbon reservoirs typically includes development andanalysis of computer simulation models of the reservoir. Theseunderground hydrocarbon reservoirs are typically complex rock formationswhich contain both a petroleum fluid mixture and water. The reservoirfluid content usually exists in two or more fluid phases. The petroleummixture in reservoir fluids is produced by wells drilled into andcompleted in these rock formations.

A geologically realistic model of the reservoir, and the presence of itsfluids, also helps in forecasting the optimal future oil and gasrecovery from hydrocarbon reservoirs. Oil and gas companies have come todepend on geological models as an important tool to enhance the abilityto exploit a petroleum reserve. Geological models of reservoirs andoil/gas fields have become increasingly large and complex.

In simulation and geological models, the reservoir is organized into anumber of individual cells. Seismic data with increasing accuracy haspermitted the cells to be on the order of 25 meters areal (x and y axis)intervals. For what are known as giant reservoirs, the number of cellsis the least hundreds of millions, and reservoirs of what is known asgiga-cell size (a billion cells or more) are encountered.

Similar considerations of data volume are also presented in seismic dataprocessing. Seismic data obtained from surveys over large areas of theearth's surface such as above giant reservoirs, has been acquired andmade available in increased volumes. In processing vast amounts of dataof all three of the types described above, processing time was animportant consideration.

Three types of computer systems have been available for processing thevast amounts of data of the types encountered in petroleum explorationand production. These are supercomputers, high performance computing(HPC) and grid computing. Typically, supercomputers are speciallydesigned for particular calculation intensive tasks. An HPC system takesthe form of a group of powerful workstations or servers, joined togetheras a network to function as one supercomputer. Grid computing involves amore loosely coupled, heterogeneous and often dispersed network ofworkstations or servers than HPC.

So far as is known, existing distributed memory HPC and grid computingsystems did not provide proper quality of service (QoS) basedcommunication because of two limitations. First, standard communicationlibraries such as Message Passing Interface (MPI) and Parallel VirtualMachine (PVM) did not provide a capability for applications to specifyservice quality for computation and communication. Second, modernhigh-speed interconnects such as Infiniband, Myrinet, Quadrics andGigabit Ethernet were optimized for performance rather than forpredictability of communication latency and bandwidth.

There has been, so far as is known, little attention given to QoScontrol in high performance and grid computing. HPC users have witnesseda dramatic increase in performance over the last ten years with regardto the HPC systems. What used to take one month of HPC computation timein the ten years ago, is now taking only a few hours to run in currentsystems.

In view of this, the simplest remedy to users for a data accuracyfailure rate during a routine computation run has been resubmitting theprocessing data run after disregarding or offlining (or what is known asfencing) the problematic node/core. However, the hours of the crashedjob were thus discarded and wasted. Moreover, in the case of a moreextensive data processing run which would require several days or evenweeks to perform, resubmitting the entire data set for processing wasrequired. This was duplicative and time consuming.

U.S. Pat. No. 7,526,418, which is owned by the assignee of the presentapplication, relates to a simulator for giant hydrocarbon reservoirscomposed of a massive number of cells. The simulator mainly used highperformance computers (HPC). Communication between the cluster computerswas performed according to conventional, standard methods, such as MPImentioned above and Open MP.

U.S. Published Patent Application No. 2011/0138396 related to a datadistribution mechanism in HPC clusters. The focus of the systemdescribed was methodology to enable data to be distributed rapidly tovarious computation nodes in an HPC cluster. Thus, the focus andteachings of this system were improving processing speed by more rapidlydistributing data to the cluster nodes.

SUMMARY OF THE INVENTION

Briefly, the present invention provides a new and improved computerimplemented method of computerized processing in a data processingsystem of data for exploration and production of hydrocarbons. The dataprocessing system includes at least one master node established as apublisher of data with an established quality of service standardprofile, a plurality of processor nodes established as subscribers toreceive data from the publisher master node, and a data memory.According to the method of the present invention, the data istransmitted from the publisher master node to the subscriber processornodes as topics for processing by subscriber processor nodes. Theestablished quality of service standard profile is also transmitted fromthe publisher master node to the subscriber processor nodes. Thetransmitted data is processed in the subscriber processor nodes, and adetermination is made regarding whether the processed data at thesubscriber processor nodes complies with the transmitted establishedquality of service standard profile from the publisher master node. Ifthe processed data so complies, the processed data which complies istransmitted from the subscriber processor nodes to the publisher masternode. If the processed data does not comply, transfer of the processeddata which does not comply with the transmitted established quality ofservice standard profile is inhibited. The processed data transmittedfrom the subscriber processor nodes is assembled in the data memory ofthe data processing system.

The present invention also provides a new and improved data processingsystem for computerized processing of data for exploration andproduction of hydrocarbons. The data processing system includes a masternode established as a publisher of data with an established quality ofservice standard profile which transmits the data from the publishermaster node as topics for processing, and also transmits the establishedquality of service standard profile from the publisher master node. Thedata processing system also includes a plurality of processor nodesestablished as subscribers to receive data from the publisher masternode. The subscriber nodes receive the data topics and the establishedquality of service standard profile from the publisher master node. Thesubscriber processor nodes process the transmitted data topics from thepublisher master node and determine whether the processed data complieswith the transmitted established quality of service standard profilefrom the publisher master node. If the processed data so complies, theprocessed data in compliance with the transmitted established quality ofservice standard profile is transmitted from the subscriber processornodes to the publisher master node. Transfer of the processed data whichdoes not comply with the transmitted established quality of servicestandard profile is inhibited. The master node assembles in the datamemory the processed data which complies with the transmittedestablished quality of service standard profile.

The present invention further provides a new and improved data storagedevice having stored in a computer readable medium computer operableinstructions for causing a data processing system to performcomputerized processing of data for exploration and production ofhydrocarbons. The data processing system includes: at least one masternode established as a publisher of data with an established quality ofservice standard profile, a plurality of processor nodes established assubscribers to receive data from the publisher master node, and a datamemory. The instructions stored in the data storage device causing thedata processing system to transmit from the publisher master node to thesubscriber processor nodes the data as topics for processing bysubscriber processor nodes, and also transmit the established quality ofservice standard profile from the publisher master node to thesubscriber processor nodes. The instructions also cause the processorsubscriber nodes to process the transmitted data, and determine whetherthe processed data at the subscriber processor nodes complies with thetransmitted established quality of service standard profile from thepublisher master node. The instructions also cause the processorsubscriber nodes to transmit the processed data which complies with thetransmitted established quality of service standard profile from thesubscriber processor nodes to the publisher master node, and to inhibittransfer of the processed data which does not comply with thetransmitted established quality of service standard profile. Theinstructions also cause the master publisher node to assemble in thedata memory of the data processing system the processed data transmittedfrom the subscriber processor nodes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a prior art data processingsystem for high performance computing.

FIG. 2 is a schematic block diagram of a data processing system for highperformance and grid computing with quality of service control accordingto the present invention.

FIG. 3 is a functional block diagram of the data processing system ofFIG. 2 configured for high performance processing with quality ofservice control according to the present invention.

FIG. 4 is a functional block diagram of a set of data processing stepsperformed in the data processing system of FIGS. 2 and 3 for highperformance processing with quality of service control according to thepresent invention.

FIG. 5 is a functional block diagram of a set of data processing stepsperformed in the data processing system of FIGS. 2 and 3 for highperformance processing with quality of service control according to thepresent invention.

FIG. 6 is a functional block diagram indicating the interactiveoperation of processors of the data processing system of FIGS. 2 and 3during performance of the data processing steps of FIGS. 4 and 5.

FIG. 7 is a plot of runtime for the data processing system shown inFIGS. 2 and 3 for high performance processing with quality of servicecontrol with different sizes of input data in comparison with prior artMPI communication protocols.

FIG. 8 is a plot of network time delay in engaging a new processor nodefor different file sizes in the data processing system of FIGS. 2 and 3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention relates to high performance and grid computing ofdata for exploration and production of hydrocarbons, such ascomputerized simulation of hydrocarbon reservoirs in the earth,geological modeling, processing of seismic survey data, and other typesof data gathered and processed to aid in the exploration and productionof hydrocarbons. For the purposes of the present invention data of theforegoing types are referred to herein as exploration and productiondata. The present invention is particularly adapted for processingexploration and production data where vast amounts of such data arepresent, such as in or around what are known as giant reservoirs.

In the drawings, FIG. 1 represents an example prior art high performancecomputing network P. The high performance computing network P isconfigured for parallel computing using the message passing interface(MPI) with a master node 10 transferring data through what are known asserial heartbeat connections over data links 12 of a management network14 to a number of processor nodes 16. The processor nodes 16 areconfigured to communicate with each other as indicated at 18 accordingto the message passing interface (MPI) standard communication libraryduring parallel computing and processing of data. As has been set forth,so far as is known, standard communication libraries such as MessagePassing Interface (MPI) and Parallel Virtual Machine (PVM) did notprovide a capability for applications to specify service quality forcomputation and communication.

With the present invention, as is shown schematically in FIG. 2 in adata processing system D a master node 20 of a CPU 22 and a group ofprocessor or worker nodes 24 operating as a network arranged for highperformance or grid computing, depending on the configuration of thenetwork, of exploration and production data. As will be set forth, thedata processing system D processes exploration and production data witha controllable specified quality of service (QoS) for the processingapplications. Data processing system D operates according to theprocessing techniques which are shown schematically in FIGS. 4, 5 and 6.Thus, high performance computing (HPC) and grid computing processing ofexploration and production data are performed without impacting orlosing processing time in case of failures. A data distribution service(DDS) standard is implemented in the high performance computing (HPC)and grid computing platforms of the data processing system D, to avoidshortcomings of message passing interface (MPI) communication betweencomputing modules, and provide quality of service (QoS) for suchapplications.

Considering now the data processing system according to the presentinvention, as illustrated in FIG. 2, the data processing system D isprovided as a processing platform for high performance computing (HPC)and grid computing of exploration and processing data. The dataprocessing system D includes one or more central processing units orCPU's 22. The CPU or CPU's 22 have associated therewith a reservoirmemory or database 26 for general input parameters, of a type and natureaccording to the exploration and production data being processed,whether reservoir simulation, geological modeling, seismic data or thelike.

A user interface 28 operably connected with the CPU 22 includes agraphical display 30 for displaying graphical images, a printer or othersuitable image forming mechanism and a user input device 32 to provide auser access to manipulate, access and provide output forms of processingresults, database records and other information.

The reservoir memory or database 26 is typically in a memory 34 of anexternal data storage server or computer 38. The reservoir database 26contains data including the structure, location and organization of thecells in the reservoir model, data general input parameters, as well asthe exploration and production data to be processed, as will bedescribed below.

The CPU or computer 22 of data processing system D includes the masternode 20 and an internal memory 40 coupled to the master node 20 to storeoperating instructions, control information and to serve as storage ortransfer buffers as required. The data processing system D includesprogram code 42 stored in memory 40. The program code 42, according tothe present invention, is in the form of computer operable instructionscausing the master node 20 and processor nodes 24 to transfer theexploration and production data and control instructions back and forthaccording to data distribution service (DDS) intercommunicationtechniques, as will be set forth.

It should be noted that program code 42 may be in the form of microcode,programs, routines, or symbolic computer operable languages that providea specific set of ordered operations that control the functioning of thedata processing system D and direct its operation. The instructions ofprogram code 42 may be stored in memory 40 or on computer diskette,magnetic tape, conventional hard disk drive, electronic read-onlymemory, optical storage device, or other appropriate data storage devicehaving a computer usable medium stored thereon. Program code 42 may alsobe contained on a data storage device as a computer readable medium.

The processor nodes 24 are general purpose, programmable data processingunits programmed to perform the processing of exploration and productiondata according to the present invention. The processor nodes 24 operateunder control of the master node 20 and the processing results obtainedare then assembled in memory 34 where the data are provided forformation with user interface 28 of output displays to form data recordsfor analysis and interpretation.

Although the present invention is independent of the specific computerhardware used, an example embodiment of the present invention ispreferably based on a master node 20 and processor nodes 24 of an HPLinux cluster computer. It should be understood, however, that othercomputer hardware may also be used.

According to the present invention, the data processing system D whosecomponents are shown in FIG. 2 is configured as shown in FIG. 3according to the data distribution service (DDS) techniques. Asindicated in FIG. 3, the master node 20 also shown in FIG. 2 isestablished as a publisher as indicated at 50 in that the master node 20is the node responsible for dissemination and distribution of theexploration and production data to be processed by the processor nodes24. The master node 20 includes a persistence service capability asshown at 51 to preserve data samples so that they can be furnished toprocessor nodes which replace failed processor nodes, as will bedescribed. The master node 20 is also as indicated at 52 in FIG. 3established as a data writer for the exploration and production datadistributed for processing, so that values of the data which isdistributed can be established.

The processor nodes 24 in operating according to data distributionservice (DDS) are each individually established as subscribers, asindicated at 54 in FIG. 3. Thus the processor nodes as subscribers areconfigured to operate as the processors responsible assigned to receivethe exploration and production data received as a result of thesubscriber relationship to the publisher 50 of the master node 20. Theprocessor nodes 24 are also configured as data readers as indicated at56 in FIG. 3. As data readers 56, the processor nodes 24 receive anallocated portion of the exploration and production data from the datawriter 52 of the master node 20.

The DDS techniques of the present invention are further explained in thedata distribution service (DDS) standard. The DDS standard provides ascalable, platform-independent, and location-independent middlewareinfrastructure to connect information producers to consumers (i.e. nodesto nodes). DDS also supports many quality-of-service (QoS) policies,such as asynchronous, loosely-coupled, time-sensitive and reliable datadistribution at multiple layers (e.g., middleware, operating system, andnetwork).

The DDS methodology implements a publish/subscribe (PS) model forsending and receiving data, events, and commands among the participantmaster node 20 and processor nodes 24 in the processing of explorationand production data according to the present invention. As will be setforth, the master node serves as a primary publisher and the processornodes 24 function as the primary subscribers. The processor nodes 24 arealso configured to transfer the processed exploration and productiondata results to the master node 20, and the master node 20 is configuredfor this purpose as a subscriber function. Thus each node of the dataprocessing system D can serve as a publisher, subscriber, or bothsimultaneously.

Nodes of the data processing system D that are producing information(publishers) create “topics” which are parameters of interest for thedata being processed, depending on the type of exploration andproduction data being processed. The nodes operating in the DDS modetake care of delivering the data samples to those subscribers thatindicate an interest in that topic. In a preferred computer networkaccording to the present invention, example implementations of DDSprovide low latency messaging (as fast as 65 microseconds between nodes)and high throughput (up to 950 Mbps).

As has been set forth, the present invention processes exploration andproduction data for giant reservoirs where vast amounts of data need tobe processed. An example type of processing performed is two-way datastreaming between master and processor nodes. Examples of data streamingusage in exploration and production data for giant reservoirs are U.S.Pat. Nos. 7,596,480; 7,620,534; 7,660,711; 7,526,418; and 7,809,537. Itshould be understood that the present invention may also be used inconnection with communication between nodes for HPC or grid computing ofexploration and production data for other types of processing inaddition to data streaming. The data communication according to thepresent invention between nodes for HPC and grid computing may also beused for other types of data, such as bioinformatics processing andcomputational fluid dynamics. The present invention is adapted for usein processing of large amounts of data which consume considerable timeand thus has an increased likelihood of system failure during the courseof processing.

FIG. 4 is a functional block diagram of a set 60 of data processingsteps performed by the master node 20 in the data processing system Daccording to the present invention. As shown at step 62 of FIG. 4, themaster node 20 is designated as the master publisher. Master node 20then spawns two threads using OpenMP to parallelize two functionalities.In the first thread, the master node 20 initializes during step 64 to bea publisher (P0) with selected QoS profile, which is predefined in anXML file. For example, in the data streaming embodiment described below,three main QoS policies are adopted. These are: durability, reliability,and history.

The “durability” of the QoS profile saves the published data topics sothat the data topics can be delivered to subscribing nodes that join thesystem at a later time, even if the publishing node has alreadyterminated. The persistence service can use a file system or arelational data base to save the status of the system.

The second QoS profile or policy, which is reliability, indicates thelevel of reliability requested by a DataReader or offered by aDataWriter. Publisher nodes may offer levels of reliability,parameterized by the number of past issues they can store for thepurpose of retrying transmissions. Subscriber nodes may then requestdifferent levels of reliable delivery, ranging from fast-but-unreliable“best effort” to highly reliable in-order delivery. Thus providingper-datastream reliability control. In case the reliability type is setto “RELIABLE”, the write operation on the DataWriter may be blocked ifthe modification would cause data to be lost or else cause one of theresource limited to be exceeded.

The third policy, history, controls the behavior of the communicationwhen the value of a topic changes before it is finally communicated tosome of its existing DataReader entities. If the type is set to“KEEP_LAST”, then the service will only attempt to keep the latestvalues of the topic and discard the older ones. In this case, aspecified value of depth of data retention regulates the maximum numberof values the service will maintain and deliver. The default (and mostcommon setting) for depth is one, indicating that only the most recentvalue should be delivered.

If the history type is set to “KEEP_ALL”, then the service will attemptto maintain and deliver all the values of the sent data to existingsubscribers. The resources that the service can use to keep this historyare limited by the settings of the RESOURCE_LIMITS QoS. If the limit isreached, then the behavior of the service will depend on the RELIABILITYQoS. If the reliability kind is “BEST_EFFORT”, then the old values willbe discarded. If the reliability setting is “RELIABLE”, then the servicewill block the DataWriter until it can deliver the necessary old valuesto all subscribers.

In the data streaming embodiment described herein, “DURABILITY” was usedand specified, because it was desirable for compute nodes to continuejoining the system whenever there is a failure in another node, and thusavoid the consequences of a node failure during an extended processingrun. In the third policy of history, “KEEP_LAST” was selected since thestreamed seismic or simulation data are not expected to change.Specifically, the seismic shots and simulation cell values are fixed andnot dynamic. In reliability QoS policy, RELIABLE, was specified as allthe values need to reach the subscribers and have complete answers ofthe data streaming. The requirement of data accuracy prohibited missingor losing elements while transmitting.

As indicated at step 66, the thread also specifies the domain where allthe publishers and subscribers would work on, which is domain-0 in thepresent embodiment. Next, as indicated at step 68, a topic with anidentifying name is specified and a DataWriter (DW-0) is initializedduring step 70 under P0 using that topic. After that, the master node 20as publisher begins reading the data matrices from input to beprocessed, and initializes the data structure for the source sample (SS)by defining the matrices dimension and the number of processor nodes 24which are designated as processor or worker nodes for the processing ofthe exploration and production data. The master node then during step 72starts sending the Source sample SS-0 to the designated worker processornodes 24 through the DataWriter DW-0.

The second thread of master node 20 reverses the function of thread 0 bycreating an instance of a subscriber S0 as indicated at step 74 with theselected QoS profile in Domain-0, in preparation to receive the partialresults from the worker nodes 24 (designated worker nodes 24 act assubscribers at the beginning and then as publishers at the end of theirprocessing). The master node 20 then listens to the worker nodes 24 toreceive processing results as indicated at 76 through the receivingsample RS-0 and verifies compliance with the selected QoS profile asindicated at step 78. The master node 20 checks the QoS profile while itis receiving data from the worker nodes. It only receives data fromthose working nodes 24 which are matching in their QoS. It will notreceive and will not negotiate with other worker nodes which haveincompatible QoS settings. The master node 20 then during step 80 storesthe verified processing results in data memory, and the stored resultsare available for output and display.

FIG. 5 is a functional block diagram of a set 84 of data processingsteps performed by the processor nodes 24 in the data processing systemD according to the present invention. On the subscribers' side (i.e.,the workers), each node 24 during step 86 initiates itself as asubscriber to the main publisher P0, assigns an ID to itself (Wi), andduring step 88 starts receiving the data for computational processing.The distribution of which data goes to which node 24 is done dynamicallyin a way that is determined by first identifying the data range taken byeach node according to the format of the data. As indicated at step 90,the QoS checking is done during the nodes' receiving process. The datais then processed during step 92 according to the processing required.Each worker node 24 during step 94 then sends its output with verifiedQoS through its DataWriter (DW-i) to the master node 20 for resultcollection. As indicated at step 96, QoS checking is done during thesending of data in step 94. Thus, when there is communication betweenthe publisher and subscriber, QoS checking is done to establish thisconnection.

FIG. 6 is a diagram 100 illustrating the interaction of the master node20 and worker processor nodes 24 in an example implementation of a datastreaming processing of exploration and production data. Theimplementation starts at step 102 by designating the master node 20 ofthe cluster as the main publisher. The master node 20, in turn, spawnstwo threads using OpenMP to parallelize its two main functions:initializing the node to be a publisher (P0) with the selected QoSprofile; and specifying during step 104 the domain which the publishersand subscribers are to work on, which is domain-0 in the exampleimplementation. Specifying the domain is necessary in order to allowmultiple groups of publishers and subscribers to work independently,segmenting the cluster into several smaller sub-clusters, if needed.Different algorithms may require different topics (i.e. datasets) to besent independently by the same publisher, and each of these topics mayhave several DataWriters for redundancy.

Next, during step 106, a topic with the name “SEND_DATA” is created anda DataWriter (DW-0) is initialized during step 108 under P0 using thecreated topic. The reason for this hierarchy is that different topics(i.e. datasets) may be required to be sent independently by the samepublisher, and each of these topics may have several DataWriters forredundancy. After that, the publisher during step 110 starts reading theseismic shots or simulation cells data from input, and initializes thedata structure for the source sample (SS) by defining the matrices datasize and the number of workers. The publisher then during step 110starts sending the source sample SS-0 through the DataWriter DW-0. Theprocedure continues until step 112 indicates all sending has beencompleted. Processing then is continued at step 114.

As indicated at step 114, the second thread from master node 20 reversesthe function of thread 0 by creating an instance of a subscriber S0 withselected QoS profile in Domain-0, in preparation to receive the partialresults from the worker or processor nodes 24. Specifically, aDataReader (DR-0) is configured during step 116 at master node 20 forthe subscriber S0 (the workers) that uses topic “RECV_RESULT”. Masternode 20 then as indicated at step 118 listens to the workers through thereceiving sample RS-0 and outputs the partial results. This processingcontinues as indicated at step 120 until receiving of sample RS-0 iscompleted.

On the subscribers' side (i.e., the workers), during step 122 eachworker node 24 as discussed above and as shown in FIG. 5 initiatesitself as a subscriber to the main publisher P0, assigns an ID to itself(Wi), and starts receiving the seismic or simulation data for processingand subsequent result collection. The distribution of which data goes towhich node is done dynamically in a way that is determined by firstidentifying the data size taken by each node according to the format ofthe data.

In case of a node failure of a processor node 24 on the workers' side,the system administrator of master node 20 may initiate a new processornode 24 with the same ID of the failed worker. The new worker would readthe written checkpointed status as defined in the policy, re-read thesample from the persistence service 51, and resume the operation of thesystem.

It is important to mention that as a requirement for the durability QoS,all sent topics require DataWriters to match the configuration of thepersistent QoS policy configuration with the DataReaders. As aconsequence, a DataWriter that has an incompatible QoS with respect towhat the topic specified will not send its data to the persistentservice, and thus its status will not be saved. Similarly, a DataReaderthat has an incompatible QoS with respect to the specified in the topicwill not get data from it.

Thus FIG. 6 illustrates an example parallel processing often encounteredwith exploration and production data according to the present invention.The results were as expected: QoS could be controlled withfault-tolerance enabled when using the DDS techniques as adoptedmiddleware in such computer processing of exploration and productiondata. Specifically, compute nodes on the cluster were turned off andback on again, and the jobs continued to run with no need for a restart.

The present invention provides the ability to control QoS properties onHPC and Grids that affect predictability, overhead, resourceutilization, and aligns the scarce resources to the most criticalrequirements to these jobs.

To evaluate the performance of the present invention over conventionalHPC and compare it with processing using MPI, data streaming wasperformed on the same data set using both paradigms (i.e., DDS with thepresent invention and MPI) and evaluated them on the data processingsystem clusters of FIGS. 1 and 2. The data streaming processingalgorithm is computationally intensive with iterations, and it waschosen since it is a fundamental operation in many numerical linearalgebra applications used in processing of exploration and productiondata. An efficient implementation on parallel computers is an issue ofprime importance when providing such systems for processing ofexploration and production data.

FIG. 7 shows benchmarks to test the scalability and runtime of MPI andDDS by streamlining Reservoir Simulation data. It can be seen that theMPI version outperformed in terms of speed by taking around 6.76 secondsto stream 10 GB size of file, compared with 7.9 seconds using DDS. Thisis expected since the QoS is adding more computations and checks to thecommunication level. As has been mentioned, however, MPI and PVM arefocused on processing speed, with no effort to monitor accuracy orpossible processing deficiencies.

FIG. 8 shows the delay in engaging a new node according to the presentinvention, replacing a crashed node, while using the persistent serviceand durability, reliability and history QoS. This test is not applicableto a prior art MPI implementation. Since MPI implementations do notprovide a capability of specifying service quality for computation orcommunication. As indicated in FIG. 8, the delay in engaging a new nodeis proportional to the size of the matrices, since the persistentservice needs to resend all the previously published instances to thisnew node. During the benchmark testing, test results depicted in FIG. 8indicate that it took 15.2 seconds in a 10 GB data stream between nodes,while it took 72.2 seconds in a 50 GB test. This is to be compared withthe time required when it was necessary to resubmit the entire data setfor processing for data in large quantities.

The present invention thus adds several benefits of having quality ofservice in high performance computing that are not available in thetraditional method (i.e. by using MPI as a middleware). Among thebenefits are that periodic publishers can indicate the speed at whichthey can publish by offering guaranteed update deadlines. By setting adeadline, a compliant publisher promises to send a new update at aminimum rate. Subscribers may then request data at that or any slowerrate.

Another benefit is that the continuing participation or activity ofentities can be monitored. The selected QoS offered with the presentinvention determines whether an entity or a node is “active” (i.e.,alive). The application can also be informed via a listener when anentity is no longer responsive. A further benefit is that the presentinvention also permits the data processing System D to automaticallyarbitrate between multiple publishers of the same topic with a parametercalled “strength.” Subscribers receive from the strongest activepublisher. This provides automatic failover; if a strong publisherfails, all subscribers immediately receive updates from the backup(weaker) publisher.

It is also to be noted that QoS parameters exist with the DDS employmentto control the resources of the entire system, suggest latency budgets,set delivery order, attach user data, prioritize messages, set resourceutilization limits and partition the system into namespaces. The presentinvention thus provides the ability to control QoS properties on HPC andgrids that affect predictability, overhead, resource utilization, andalign the computational resources to the most critical requirements.

The present invention is a feasible option for those applications inwhich QoS is considered a priority, or for those HPC batch jobs thatwould run for several days on commodity hardware, where the probabilityof failure is not negligible. Accordingly, the present inventionprovides the ability to control QoS properties on HPC and grids thataffect predictability, overhead, resource utilization, and aligns thescarce resources to the most critical requirements.

The invention has been sufficiently described so that a person withaverage knowledge in the matter may reproduce and obtain the resultsmentioned in the invention herein Nonetheless, any skilled person in thefield of technique, subject of the invention herein, may carry outmodifications not described in the request herein, to apply thesemodifications to a determined structure, or in the manufacturing processof the same, requires the claimed matter in the following claims; suchstructures shall be covered within the scope of the invention.

It should be noted and understood that there can be improvements andmodifications made of the present invention described in detail abovewithout departing from the spirit or scope of the invention as set forthin the accompanying claims.

What is claimed is:
 1. A computer implemented method of computerizedprocessing in a data processing system of data for exploration andproduction of hydrocarbons, the data processing system including atleast one master node established as a publisher of exploration andproduction data with an established quality of service standard profileincluding a reliability policy for the exploration and production databeing processed, a plurality of processor nodes established assubscribers to receive exploration and production data from thepublisher master node, and a data memory, the method comprising thecomputer processing steps of: (a) transmitting the established qualityof service standard profile from the publisher master node to thesubscriber processor nodes; (b) establishing with the publisher masternode a domain for exploration and production processing by the publishermaster node and designated ones of the plurality of processor nodes assubscriber processor nodes; (c) further establishing the designatedsubscriber processor nodes as data writers to transfer to the publishermaster node the processed exploration and production data; (d) sending asource data sample of the exploration and production data from thepublisher master node to the designated subscriber processor nodes ofthe domain; (e) processing the transmitted exploration and productiondata in the designated subscriber processor nodes of the domain; (f)monitoring at the publisher master node the processed exploration andproduction data of the designated subscriber processor nodes of thedomain; (g) determining in the publisher master node whether thedesignated subscriber processor nodes of the domain comply with thetransmitted established quality of service standard profile from thepublisher master node; and (h) if so, receiving at the publisher masternode the processed exploration and production data from the designatedsubscriber processor nodes which comply with the transmitted establishedquality of service standard profile; and (i) if not, inhibiting at thepublisher master node transfer to the publisher master node of theprocessed exploration and production data from the designated subscriberprocessor nodes which do not comply with the transmitted establishedquality of service standard profile; and (j) assembling in the datamemory of the data processing system the processed exploration andproduction data received at the publisher master node.
 2. The computerimplemented method of claim 1, wherein the data processing systemfurther includes a data display, and further including the computerprocessing step of: forming an output display of the assembled processedexploration and production data.
 3. The computer implemented method ofclaim 1, wherein the exploration and production data comprises areservoir simulation model.
 4. The computer implemented method of claim1, wherein the exploration and production data comprises a geologicalmodel of a reservoir.
 5. The computer implemented method of claim 1,wherein the exploration and production data comprises a seismic surveyof the earth in a reservoir.
 6. The computer implemented method of claim1, wherein the publisher master node is further established as a datawriter for transmitting a plurality of sets of the exploration andproduction data as domains for processing by the designated subscriberprocessor nodes.
 7. The computer implemented method of claim 6, whereinthe designated subscriber processor nodes are further established asdata readers for receiving and processing selected ones of the aplurality of sets of the exploration and production data as domainstransmitted by the publisher master node.
 8. The computer implementedmethod of claim 1, wherein the publisher master node is furtherestablished as a data reader for receiving and transferring to the datamemory the processed exploration and production data received from thedata writers of the designated subscriber processor nodes.
 9. Thecomputer implemented method of claim 8, wherein the reliability policyof the established quality of service standard profile comprises anindication of the level of reliability of delivery required for thedesignated subscriber processor nodes as data writers for transfer ofexploration and production data, and further including the steps of: (a)requesting with the data writer designated subscriber processor nodesthe indication of the level of reliability according to the reliabilitypolicy; and (b) sending the processed exploration and production datafrom the data writer designated subscriber processor nodes to thepublisher master node as data reader with the indicated level ofreliability required according to the reliability policy.
 10. A dataprocessing system for computerized processing of data or exploration andproduction of hydrocarbons, the data processing system comprising amaster ode, a plurality of processor nodes and a data memory, the dataprocessing further comprising: (a) the master node established as apublisher of exploration and production data with an established qualityof service standard profile including a reliability policy for theexploration and production data being processed, the master nodeperforming the steps of: (1) transmitting the established quality ofservice standard profile from the publisher master node to the processornodes; (2) establishing with the publisher master node a domain forexploration and production processing by the publisher master node anddesignated ones of the plurality of processor nodes as subscriberprocessor nodes; (3) further establishing the designated subscriberprocessor nodes as data writers to transfer to the publisher master nodethe processed exploration and production data; (4) sending a source datasample of the exploration and production data from the publisher masternode to the designated subscriber processor nodes of the domain; (b) theplurality of processor nodes established as subscribers to receiveexploration and production data from the publisher master node, theplurality of processor nodes performing the steps of: (1) receiving inthe designated subscriber processor nodes of the domain the explorationand production data and the established quality of service standardprofile from the publisher master node; (2) processing the transmittedexploration and production data in the designated subscriber processornodes of the domain; and (c) the master node further performing thesteps of: (1) monitoring the processed exploration and production dataof the designated subscriber processor nodes of the domain; (2)determining whether the designated subscriber processor nodes of thedomain comply with the transmitted established quality of servicestandard profile from the publisher master node; (3) if so, receivingthe processed exploration and production data from the designatedsubscriber processor nodes which comply with the transmitted establishedquality of service standard profile; and (4) if not, inhibiting at thepublisher master node transfer of the processed exploration andproduction data from the designated subscriber processor nodes which donot comply with the transmitted established quality of service standardprofile; and (5) assembling in the data memory the processed explorationand production data from the designated subscriber processor nodes whichcomply with the transmitted established quality of service standardprofile.
 11. The data processing system of claim 10, further including adata display, and wherein the master node further performs the step of:forming an output display of the assembled processed exploration andproduction data.
 12. The data processing system of claim 10, wherein theexploration and production data comprises a reservoir simulation model.13. The data processing system of claim 10, wherein the exploration andproduction data comprises a geological model of a reservoir.
 14. Thedata processing system of claim 10, wherein the exploration andproduction data comprises a seismic survey of the earth in a reservoir.15. The data processing system of claim 10, wherein the publisher masternode is further established as a data writer for transmitting differentsets of the exploration and production data as domains for processing bythe designated subscriber processor nodes.
 16. The data processingsystem of claim 15, wherein the designated subscriber processor nodesare further established as data readers for receiving and processingselected ones of the different sets of the exploration and productiondata as domains transmitted by the publisher master node.
 17. The dataprocessing system of claim 10, wherein the publisher master node isfurther established as a data reader for receiving and transferring tothe data memory the processed exploration and production data receivedfrom the data writers of the designated subscriber processor nodes. 18.The data processing system of claim 17, wherein the reliability policyof the established quality of service standard profile comprises anindication of the level of reliability of delivery required for thedesignated subscriber processor nodes as data writers for transfer ofexploration and production data, and further including the steps of: (a)requesting the indication of the level of reliability according to thereliability policy; and (b) sending the processed exploration andproduction data to the publisher master node as data reader with theindicated level of reliability required according to the reliabilitypolicy.
 19. A data storage device having stored in a non-transitorycomputer readable storage medium computer operable instructions forcausing a data processing system to perform computerized processing ofdata for exploration and production of hydrocarbons, the data processingsystem including at least one master node established as a publisher ofexploration and production data with an established quality of servicestandard profile including a reliability policy for the exploration andproduction data being processed, a plurality of processor nodesestablished as subscribers to receive the exploration and productiondata from the publisher master node, and a data memory, the instructionsstored in the data storage device causing the data processing system toperform the following steps: (a) transmitting the established quality ofservice standard profile from the publisher master node to thesubscriber processor nodes; (b) establishing with the publisher masternode a domain for exploration and production processing by the publishermaster node and designated ones of the plurality of processor nodes assubscriber processor nodes; (c) further establishing the designatedsubscriber processor nodes as data writers to transfer to the publishermaster node the processed exploration and production data; (d) sending asource data sample of the exploration and production data from thepublisher master node to the designated subscriber processor nodes ofthe domain; (e) processing the transmitted exploration and productiondata in the designated subscriber processor nodes of the domain; (f)monitoring at the publisher master node the processed exploration andproduction data of the designated subscriber processor nodes of thedomain; (g) determining in the publisher master node whether thedesignated subscriber processor nodes of the domain comply with thetransmitted established quality of service standard profile from thepublisher master node; and (h) if so, receiving at the publisher masternode the processed exploration and production data from the designatedsubscriber processor nodes which comply with the transmitted establishedquality of service standard profile; and (i) if not, inhibiting at thepublisher master node transfer to the publisher master node of theprocessed exploration and production data from the designated subscriberprocessor nodes which do not comply with the transmitted establishedquality of service standard profile; and (j) assembling in the datamemory of the computer system the processed exploration and productiondata received at the publisher master node.
 20. The data storage deviceof claim 19, wherein the data processing system further includes a datadisplay, and wherein the instructions further include instructionscausing the data processing system to perform the step of: forming anoutput display of the assembled processed exploration and productiondata.
 21. The data storage device of claim 19, wherein the explorationand production data comprises a reservoir simulation model.
 22. The datastorage device of claim 19, wherein the exploration and production datacomprises a geological model of a reservoir.
 23. The data storage deviceof claim 19, wherein the exploration and production data comprises aseismic survey of the earth in a reservoir.
 24. The data storage deviceof claim 19, wherein the publisher master node is further established asa data writer and wherein the instructions further include instructionscausing the publisher master node to perform the step of transmittingdifferent sets of the exploration and production data as domains forprocessing by the designated subscriber processor nodes.
 25. The datastorage device of claim 24, wherein the designated subscriber processornodes are further established as data readers and wherein theinstructions further include instructions causing the designatedsubscriber processor nodes to perform the step of receiving andprocessing selected ones of the different sets of the exploration andproduction data as domains transmitted by the publisher master node. 26.The data storage device of claim 19, wherein the publisher master nodeis further established as a data reader and wherein the instructionsfurther include instructions causing the publisher master node as datareader to perform the step of receiving and transferring to the datamemory the processed exploration and production data received from thedata writers of the designated subscriber processor nodes.
 27. The datastorage device of claim 26, wherein the reliability policy of theestablished quality of service standard profile comprises an indicationof the level of reliability of delivery required for the designatedsubscriber processor nodes as data writers for transfer of explorationand production data, and further including the steps of: (a) requestingwith the data writer designated subscriber processor nodes theindication of the level of reliability; and (b) sending the processedexploration and production data from the data writer designatedsubscriber processor nodes to the publisher master node as data readerwith the indicated level of reliability.